If you recall from Chapter 5, Divide and Conquer – Classification Using Decision Trees and Rules, a decision tree builds a model, much like a flowchart, in which decision nodes, leaf nodes, and branches define a series of decisions that are used to classify examples. Such trees can also be used for numeric prediction by making only small adjustments to the tree growing algorithm. In this section, we will consider the ways in which trees for numeric prediction differ from trees used for classification.
Trees for numeric prediction fall into two categories. The first, known as regression trees, were introduced in the 1980s as part of the seminal classification and regression tree (CART) algorithm. Despite the name, regression trees do not use linear regression methods as described earlier in this chapter; rather, they make predictions based on the average value of examples that reach a leaf.
